NVH Design, Analysis and Optimization of Chevrolet Bolt Battery Electric Vehicle

2018-01-0994

04/03/2018

Features
Event
WCX World Congress Experience
Authors Abstract
Content
A multi-stage system level method is used to design, optimize and enhance electric motor NVH performance of General Motors’ Chevrolet Bolt battery electric vehicle (BEV). First, the rotor EM (electromagnetic) design optimizes magnet placement between adjacent poles asymmetrically, along with a pair of small slots stamped near the rotor outer surface to lower torque ripple and radial force. The size and placement of stator slot openings under each pole are optimized to lower torque ripple and radial force. Next, motor stator level FE (Finite Element) analysis and modal test correlation are performed to benchmark the orthotropic stator material properties and accurately predict modal results within 7% error below 2 kHz. Furthermore, tangential and radial EM forces are applied on motor-in-fixture subsystem FE model, which predicts surface vibration and pseudo sound power on the motor housing. Analysis results are validated by test data, and are used to benchmark electric motor as BEV noise source. Analysis also helps to identify key motor orders and rpm for NVH optimization. Lastly, optimized EM and motor mechanical designs are modeled in the drive unit (DU) for transmission level NVH analysis. The multi-stage system level model is used to study key design parameters like EM force coupling with structural modes, motor mounting design, DU ribbing and stiffness optimization. Key design concepts and parameters that have most influence on radiation sound power from DU are identified, and subsequently optimized for improved noise performance of Bolt EV.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-0994
Pages
5
Citation
He, S., "NVH Design, Analysis and Optimization of Chevrolet Bolt Battery Electric Vehicle," SAE Technical Paper 2018-01-0994, 2018, https://doi.org/10.4271/2018-01-0994.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-0994
Content Type
Technical Paper
Language
English